Delayed Diagnosis of Multiple Sclerosis, Treatment Initiation and Non-adherence in Upper Egypt
- Conditions
- Multiple Sclerosis, Relapsing-Remitting
- Registration Number
- NCT05438693
- Lead Sponsor
- doaa mokhtar mahmoud
- Brief Summary
The earlier that MS can be diagnosed; the sooner treatment can be initiated with timely reduction of subclinical disease activity and prevention of disability progression.
However, significant delays can still occur between noticing the first symptoms and receiving a diagnosis even before a person with symptoms suggestive of MS sees a neurologist. Such delays could be due to heterogeneity of clinical and imaging manifestations, which not only differ between patients, but also vary in individual patients over time. Moreover, lack of awareness of the primary care physicians about MS presentations, the limited accessibility to specialized centers or the non-availability of diagnostic tools such as MRI scanners and lumbar puncture, may further add to this delay and increases the risk of disability.
There are also many factors that can contribute to delayed initiation of DMT after diagnosis like inadequate knowledge with DMT, their high coast and limited access to health care insurance services.
Like many chronic conditions, non- Adherence to drug therapies is estimated up to 50%, with associated increased morbidity, mortality, and health care costs.
To the best of our knowledge, this is the first study in upper Egypt that tries to address these factors. By conducting this study, we aim at identifying factors leading to delayed diagnosis of MS, initiation and adherence to DMT in order to translate recent advances in the diagnosis and treatment of MS into improved outcomes in the lives of people with MS and their families and to avoid many of the long-term economic and personal costs that result from unnecessary irreversible disability.
- Detailed Description
this is cross-sectional study in the Neuropsychiatry department, South Valley university hospitals, where patients with remitting relapsing multiple sclerosis according to McDonald criteria 2017 using disease modifying treatment for at least 3 months are recruited. patients were recruited from 3 major MS clinics in Assiut, South valley and Luxor.
each patient was subjected to the following:
* Full history and neurological examination including EDSS scores.
* MRI brain and spine.
* Electrophysiologic study: VEP, ABR, SSEP.
* clinical scales:
1. The Arabic version of the eight-item Morisky Medication Adherence Scale (MMAS-8).
2. Treatment Satisfaction Questionnaire for Medication (TSQM-9).
3. Extended disability status scale (EDSS) measurement for MS patients
4. Hamilton depression rating-17-item version (HAM-D 17).
5. Fatigue Severity Scale (FSS) for determination of fatigue degree using the Arabic validated version
6. The Pittsburgh Sleep Quality Index (PSQI)
7. International Restless Legs Syndrome rating scale time to diagnosis and DMT initiation was calculated and the rate of adherence was determined this was followed be determining factors related to each Data analysis was done using the IBM Statistical Package for Social Sciences software package version 20.0. (Armonk, NY: IBM Corp). The qualitative variables were described with ratio and percentage. The Shapiro-Wilk test was used to verify the normality of distribution. Quantitative variables with normal distribution were reported with mean and SD, and those without normal distribution were described using median and range of mid-quartiles. The level of statistical significance was set at p \< 0.05.
The used tests were
1. - Chi-square test: For categorical variables, to compare between different groups
2. - Monte Carlo correction: Correction for chi-square when more than 20% of the cells have expected count less than 5
3. - Mann Whitney test: For abnormally distributed quantitative variables, to compare between two studied categories
4. - Kruskal-Wallis test: For abnormally distributed quantitative variables, to compare between more than two studied categories
5. - Regression: univariate and multivariate logistic regression for factors related to delayed diagnosis, delayed treatment initiation and non-adherence
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 80
- Patient with confirmed diagnosis of remitting relapsing multiple sclerosis according to the 2017 McDonald's criteria and receiving any DMT for at least 3 months.
- Presence of any clinical or radiological finding suggesting a diagnosis other than MS or other demyelinating diseases.
- Presence of other systemic disease or being on long term treatment for any disease.
- Incomplete clinical or radiological data were provided.
- Subject declined to provide written informed consent.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method rate of adherence to disease modifying treatment among MS patients in the last month period prior to interview the rate of adherence to DMT among MS patients is measured by Morinski medical adherence scale for medications. with a score of 8 is considered adherent while \<8 is considered non-adherent.
time to diagnosis of multiple sclerosis From date of first documented MS symptom until the date of first documented diagnosis, assessed up to 10 years from onset symptoms time to MS diagnosis is measured from the date of the first symptom till the date of the official diagnosis of MS
time to disease modifying treatment initiation From date of first documented diagnosis until the date of first documented date of start of DMT, assessed up to 10 years from diagnosis time to DMT initiation is calculated from the date of diagnosis till the date the patient first received the prescribed DMT
- Secondary Outcome Measures
Name Time Method determine factors related to delayed diagnosis of MS From date of first documented MS symptom until the date of first documented diagnosis, assessed up to 10 years from onset symptoms after reviewing literature, a number of factors related to delayed diagnosis of MS were determined including (age, sex, residence, education, marital status, first presenting symptom, year of presentation , first medical specialty sought and previous misdiagnosis).
the frequency of those risk factors in patients with both early and delayed diagnosis were calculated and bivariate and multivariable analysis indicated the possible independent predictive factors to the diagnostic delayfrequency of depression as a factor related to DMT adherence in the last month period prior to interview the frequency of depression (using Hamilton depression scale-21), is measured and correlated to adherence. The score range from 0-50, Higher scores on this scale (\>=8) are indicative of depression.
frequency of fatigue as a factor related to DMT adherence in the last month period prior to interview the frequency of fatigue (using the fatigue severity scale-9), is measured and correlated to adherence. the score range from 1-63, Higher scores on this scale (\>=36) are indicative of fatigue
frequency of sleep disturbances as a factor related to DMT adherence in the last month period prior to interview the frequency of sleep disturbances (using Pittsburgh Sleep Quality Index), is measured and correlated to adherence. It consists of 19 self- rated questionnaires and 5 additional questionnaires for bed partner. It provides a global score of sleep on a scale from 1 to 21, with higher scores indicating more sleep complaints.
treatment satisfaction as a factor related to DMT adherence in the last month period prior to interview treatment satisfaction (using Treatment Satisfaction Questionnaire for Medication-9), is measured and correlated to adherence. Questions comprise four aspects representing the personal impressions of treatment benefit, tolerability, convenience of use, and general satisfaction with the treatment. scores for each of the TSQM subscales ranged from 0 to 100. total Scores obtained were summed and the percentage of satisfaction score was calculated, where higher scores indicated higher satisfaction
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Trial Locations
- Locations (2)
Assiut University
🇪🇬Assiut, Egypt
South valley University
🇪🇬Qena, Egypt
Assiut University🇪🇬Assiut, Egypt